Abstract
In an electric vehicle technology, the battery is an integral component of the system. The available charge of the battery is represented in terms of the state of charge (SOC). A battery management system (BMS) is implemented in EV to calculate the accurate SOC and to control the battery behaviour. In literature, there are different methods mentioned to estimate SOC of battery. However, these proposed methods have many limitations like complex algorithm, high computation cost. Out of all these methods the major drawbacks is that it has not considered the effect of factors like aging, temperature, internal chemical composition, it’s charging/discharging rate on SOC estimation of the battery. Against this background, the paper proposes a method to analyze the effect of these factors on SOC for its accurate estimation. For highlighting the effect of the temperature on the SOC, an existing state-space model of battery is reconstructed using the temperature coefficient. For estimation purpose, an extended Kalman filter (EKF) method is employed in the modified battery model. Further, it is implemented in the MATLAB environment and obtained results help to analyze the impact of temperature on open-circuit voltage (OCV) and SOC of the battery
Published Version
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
